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Support Vector Machines

In: Data Mining in Agriculture

Author

Listed:
  • Antonio Mucherino

    (University of Florida)

  • Petraq J. Papajorgji

    (University of Florida)

  • Panos M. Pardalos

    (University of Florida)

Abstract

Support vector machines (SVMs) are supervised learning methods used for classification [30, 41, 232]. This is one of the techniques among the top 10 for data mining [237]. In their basic form, SVMs are used for classifying sets of samples into two disjoint classes, which are separated by a hyperplane defined in a suitable space. Note that, as consequence, a single SVM can only discriminate between two different classifications. However, as we will discuss later, there are strategies that allow one to extend SVMs for classification problems with more than two classes [232, 220]. The hyperplane used for separating the two classes can be defined on the basis of the information contained in a training set.

Suggested Citation

  • Antonio Mucherino & Petraq J. Papajorgji & Panos M. Pardalos, 2009. "Support Vector Machines," Springer Optimization and Its Applications, in: Data Mining in Agriculture, chapter 0, pages 123-141, Springer.
    • Handle: RePEc:spr:spochp:978-0-387-88615-2_6
    DOI: 10.1007/978-0-387-88615-2_6
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